Composition analysis of ceramic raw materials using laser-induced breakdown spectroscopy and autoencoder neural network-Reference-Cited by-同舟云学术

Composition analysis of ceramic raw materials using laser-induced breakdown spectroscopy and autoencoder neural network

Published:2022 Issue:13 Volume:14 Page:1320-1328
ISSN:1759-9660
Container-title:Analytical Methods
language:en
Short-container-title:Anal. Methods

Author:

Lv Zunji¹²³⁴,Yu Hongxia⁴,Sun Lanxiang¹²³^ORCID,Zhang Peng¹²³

Affiliation:

1. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China

2. Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang, 110016, China

3. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China

4. Shenyang University of Technology, Shenyang 110870, China

Abstract

We proposed a spectral data feature extraction method that combines the linear regression and sparse and under-complete autoencoder. LR + SUAC can effectively extract the important information in the secondary features.

Funder

National Natural Science Foundation of China

Publisher

Royal Society of Chemistry (RSC)

Subject

General Engineering,General Chemical Engineering,Analytical Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2022/AY/D1AY02189C

Reference43 articles.

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2. Quantitative determination of chlorides by molecular laser-induced breakdown spectroscopy

3. Progress of Chemometrics in Laser-induced Breakdown Spectroscopy Analysis

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